Lead Applied AI Software Engineer ( AI)

Humana • Louisville, NY

1d • Hybrid

About The Position

The Enterprise AI organization at Humana is a pioneering force, driving AI development across our Insurance and CenterWell business segments. By collaborating with world-leading experts, we are at the forefront of delivering cutting-edge AI technologies for improving care quality and experience of millions of consumers. Our goal is to create safe AI solutions that will revolutionize and improve healthcare experience and outcomes for our customers. We are actively seeking top talent to join us in shaping the future of healthcare through AI excellence. Join our rapidly expanding team of dedicated product managers, data scientists, engineers, policy experts, and business leaders as we work together to build impactful and beneficial AI systems. At Humana, applied artificial intelligence is central to driving intelligent automation that reduces administrative burden, enabling personalization that delivers tailored member experiences, and optimizing operational efficiency across the complex healthcare ecosystem. We are seeking an accomplished Lead Applied AI Engineer. This engineer will architect and deliver advanced AI systems. These systems will seamlessly integrate Generative AI capabilities and agents. They will integrate into secure, scalable healthcare platforms. These platforms handle millions of member interactions. They maintain the highest standards of data privacy and system reliability. This highly technical and influential role defines technical standards for AI deployment across the organization. It ensures that AI systems are reliable through rigorous testing and monitoring, measurable through comprehensive metrics and evaluation frameworks. Additionally, it ensures compliance with healthcare regulations and ethical guidelines, and strategic alignment with enterprise architecture and business strategy. The Lead Applied AI Engineer will operate at the critical intersection of AI innovation and responsible healthcare technology, balancing the rapid pace of AI advancement with the careful, deliberate approach required in healthcare environments.

Requirements

• Over 7 years of experience in software engineering with a strong focus on applied AI/ML.
• Experience building and operating distributed systems at scale.
• Experience developing full-stack architectures that combine backend services with modern web applications.
• Leadership of significant projects that have delivered measurable business impact through AI capabilities.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience through significant technical leadership in AI projects, recognized contributions to the AI engineering community, or progressive career advancement into increasingly responsible AI technical leadership roles.
• Demonstrated deep expertise designing and deploying production-grade generative AI systems, including sophisticated RAG architectures with multi-hop retrieval and reasoning, agent orchestration frameworks that coordinate multiple AI agents with tool use and memory, multi-model systems that combine different AI capabilities, and conversational AI systems that maintain context and handle complex dialogues.
• Experience managing complex AI initiatives across multiple teams with different specializations, translating high-level business objectives into concrete AI system designs and technical roadmaps, and coordinating implementation across frontend, backend, data, and infrastructure teams.
• Experience driving projects from conception through production deployment and ongoing optimization.
• Strong technical proficiency in Python, including advanced language features and design patterns.
• Extensive experience with modern web application frameworks, such as React and FastAPI, and familiarity with best practices for scalability and maintainability.
• Deep knowledge of AI-specific technologies, including vector databases, embedding models, LLM APIs, and orchestration frameworks.
• Demonstrated experience establishing organization-wide best practices for prompt engineering, including systematic testing and version control, comprehensive evaluation frameworks that combine automated metrics with human assessment, model observability including tracking of costs and performance, and performance benchmarking methodologies.
• Deep familiarity with responsible AI principles, including fairness, accountability, transparency, and ethics.
• Understanding of governance considerations for AI systems, including model risk management and validation requirements.
• Practical experience addressing deployment challenges in regulated environments, including testing, documentation, change management, and ongoing monitoring requirements.

Nice To Haves

• Technical direction across organizational boundaries.
• Proven mentoring and coaching abilities to develop engineering talent.
• Strong cross-functional collaboration skills enabling effective partnership with various stakeholders, including product management, data science, design, security, compliance, and business stakeholders.
• Experience in healthcare industries.

Responsibilities

• Architect comprehensive end-to-end AI systems, including sophisticated RAG pipelines with multi-stage retrieval and re-ranking. These pipelines are designed with appropriate modularity, extensibility, and operational characteristics to support evolving business requirements. Additionally, the systems include complex agent orchestration systems that coordinate multiple specialized agents. Furthermore, they feature multi-model integrations that leverage different AI models for their respective strengths.
• Define rigorous standards for prompt engineering, including templates, versioning, and testing methodologies.
• Establish comprehensive evaluation metrics that capture both technical performance and business value.
• Develop performance optimization strategies, including model selection criteria, caching approaches, and resource utilization patterns, that teams across the organization can adopt to accelerate AI delivery.
• Lead deployment of AI systems into production environments with strong observability. This includes detailed logging and tracing. Comprehensive reliability is also crucial, featuring graceful degradation and circuit breakers. Monitoring is essential, with real-time dashboards and automated alerting. Additionally, robust incident response procedures are necessary. The goal is to ensure AI services meet stringent service level objectives required for healthcare applications.
• Design scalable data ingestion architectures that can process diverse data sources, including structured databases, unstructured documents, and real-time streams.
• Implement efficient retrieval architectures using vector databases and hybrid search approaches.
• Develop data preprocessing pipelines that clean and enrich data for AI consumption.
• Establish data quality monitoring to ensure AI systems operate on high-quality inputs.
• Drive quantitative evaluation and continuous improvement of AI systems through establishment of evaluation frameworks. Implement A/B testing capabilities, analyze user feedback and system telemetry, and systematically iterate on prompts, retrieval strategies, and model configurations. This progressive iteration improves system performance and user satisfaction over time.
• Collaborate strategically with platform teams to ensure infrastructure readiness for demanding AI workloads. This includes ensuring GPU availability, appropriate networking configurations, and optimized data storage.
• Define requirements for AI-specific platform capabilities, such as model serving infrastructure and feature stores.
• Partner on integration of AI systems with enterprise services.
• Mentor engineers at various levels through technical guidance, code reviews, architecture discussions, and career development support.
• Elevate AI engineering best practices across the organization through creation of documentation, delivery of training sessions, and establishment of communities of practice.
• Foster a culture of responsible AI development that prioritizes ethics, transparency, and user benefit.
• Ensure AI solutions rigorously meet healthcare compliance requirements through comprehensive documentation of system behavior and decision logic. Implementation of ethical standards prevents algorithmic bias and ensures fairness across different populations. Adherence to regulatory frameworks, including HIPAA, FDA guidance for clinical decision support, and emerging AI-specific regulations, is also crucial.

Benefits

• medical
• dental
• vision
• 401(k) retirement savings plan
• time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave)
• short-term and long-term disability
• life insurance

What This Job Offers

• Job Type: Full-time
• Career Level: Senior